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2. Nextdoor - Wikipedia

   en.wikipedia.org/wiki/Nextdoor

   Nextdoor's less stringent privacy and safety policies provoked concerns among some users of Streetlife—for example, full names and street addresses are visible to all members of a Nextdoor, but not Streetlife, neighbourhood—some reporting ensuing harm, such as abusive former partner finding out a woman's current address.

3. AOL Mail

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   Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!

4. News, Politics, Sports, Mail & Latest Headlines - AOL.com

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   Discover the latest breaking news in the U.S. and around the world — politics, weather, entertainment, lifestyle, finance, sports and much more.

5. AOL

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   The search engine that helps you find exactly what you're looking for. Find the most relevant information, video, images, and answers from all across the Web. AOL.

6. AOL Search - AOL Help

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   AOL Search delivers comprehensive listings and one-click access to relevant videos, pictures, local maps and more.

7. People search site - Wikipedia

   en.wikipedia.org/wiki/People_Search_site

   A people search site or people finder site is a specialized search engine that searches information from public records, data brokers and other sources to compile reports about individual people, usually for a fee. [1] [2] Early examples of people search sites included Classmates.com [3] and Whitepages.com. [4]

8. Very large-scale neighborhood search - Wikipedia

   en.wikipedia.org/wiki/Very_large-scale...

   In large neighborhood search techniques, the possible changes from one solution to its neighbor may allow tens or hundreds of values to change, and this means that the size of the neighborhood may itself be sufficient to allow the search process to avoid or escape local minima, though additional meta-heuristic techniques can still improve ...

9. Nearest neighbor search - Wikipedia

   en.wikipedia.org/wiki/Nearest_neighbor_search

   Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.